The custom computational approach identifies four subtypes of Alzheimer’s

By Shania Kennedy

– Researchers presenting at this year’s Society of Nuclear Medicine and Molecular Imaging (SNMMI) annual meeting demonstrated that a computational model can accurately identify four subtypes of Alzheimer’s disease, which may help generate insights into the underlying biology of the condition and tailor future treatment methods.

The model uses a combination of genomic and tau positron emission tomography (PET) imaging data, which are analyzed using a sparse canonical correlation analysis (SCCA)-based clustering structure to identify genome variations associated with the ‘Alzheimer’s and report the subtypes of the disease.

Studying the genes associated with Alzheimer’s subtypes is critical to informing diagnostic and treatment approaches, as the disease is genetically complex, the researchers indicated. Pathological markers of Alzheimer’s, such as PET imaging of amyloid plaques and tau neurofibrillary tangles, can help further characterize the condition.

“By identifying different subtypes of Alzheimer’s disease using both imaging and genomic information, researchers could gain potential new insights into the underlying biology of the disease and its progression,” said Joyita Dutta, PhD, associate professor in the Department of biomedical engineering from the University of Massachusetts Amherst, in a press release discussing the research. “Understanding the specific genetic associations for each subtype could also lead to the development of personalized therapeutic approaches in the future.”

In the study, the research team analyzed imaging and genomics data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants who had undergone 18F-flortaucipir PET and single nucleotide polymorphism (SNP) genotyping.

The study cohort consisted of a total of 541 individuals, 334 of whom were cognitively normal and 207 with cognitive impairment.

From PET tau imaging, the researchers calculated PET tau standardized uptake value ratios from ten large brain regions. By SNP genotyping, the research team captured 145 genome variations associated with Alzheimer’s disease.

After applying the SCCA clustering framework to these datasets, the model identified four subtypes of Alzheimer’s disease: medial temporal lobe (MTL) dominant, posterior, MTL-sparing, and lateral-temporal.

The major genes associated with each subtype were also identified in the analysis.

These findings could have significant implications for future research, diagnostics and treatments for not only Alzheimer’s disease but other conditions as well, the research team concluded.

“Individualized subtyping guided by genomics and imaging is vital for Alzheimer’s because different subtypes may also have distinct rates and profiles of cognitive decline, potentially affecting clinical trial outcomes and treatment response,” said Dutta. “By combining molecular imaging information with genomics, we created a diagnostic technique that could be truly personalized for each patient. This has the potential for broad diagnostic utility in many types of disease, not just Alzheimer’s disease.”

This research adds to a growing number of investigations examining how genomic data and advanced analytical technologies can support precision medicine efforts for diseases like Alzheimer’s.

Many of these efforts are driven by the realization that early diagnosis can minimize the impact of Alzheimer’s disease and other dementias, resulting in advances in blood diagnostics, pharmacological interventions and deep brain stimulation.

However, these interventions are often still in the research and development stage, or not widely accessible to patients, leading health systems to opt for other approaches.

One such approach is artificial intelligence (AI)-based digital screening in primary care to help flag early cognitive decline.

Indiana University School of Medicine and Indiana University Health are piloting this approach in collaboration with the Davos Alzheimer’s Collaborative (DAC), in an effort to move cognitive assistance from reactive to proactive.

In March, Jared Brosch, MD, neurologist at IU Health and assistant professor of clinical neurology at IU School of Medicine, and Phyllis Ferrell, global head of Alzheimer’s external engagement at Eli Lilly & Company and director of DAC Healthcare System Preparedness initiative, spoke with HealthITAnalytics about the pilot project and how it could impact outcomes for patients with Alzheimer’s disease and other forms of dementia.

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